Integrated Knowledge Management Framework for Clinical Genomics

临床基因组学综合知识管理框架

• 批准号: 10662487
• 负责人: Robert Richard Freimuth
• 金额: $ 47.7万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-10 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10662487
• 关键词: Acceleration; Area; Automobile Driving; Award; Clinical; Clinical Management; Collaborations; Communities; Complex; Data; Decision Making; Development; Diagnosis; Diagnostic; Electronic Health Record; Genomic medicine; Genomics; Health; Healthcare Systems; Human; Information Management; Knowledge; Knowledge Management; Learning; Life; Medical; Methods; Modeling; Modernization; Nature; Patients; Process; Reporting; Research; Research Support; Resources; Science; Speed; Standardization; System; Technology; Testing; Therapeutic; Translating; Translations; clinical practice; data sharing; genomic data; improved; knowledge integration; novel strategies; precision medicine; programs; rapid growth; structured data

PROJECT SUMMARY/ABSTRACT The rapid growth of genomic medicine reflects increases in our understanding of how genomics impacts health. Advances in the clinical utility of patient genomic information to inform diagnostic and therapeutic decision-making are driving increases in testing and the subsequent reporting of genomic data to electronic health records (EHRs) and other clinical systems. Germline genomic data are presumed to be stable over a patient’s life, but the interpretation of those data and its clinical utility will continue to evolve as new discoveries are made and translated to clinical practice. Furthermore, modern interpretations of genomic data often include multi-step processes in which one interpretation is used to inform another. The temporally dynamic nature of genomic information and knowledge (GIK) and the inter-dependencies among interpretations make a formal knowledge model for those data essential. Existing approaches to represent and manage GIK are not sufficient to fully support the needs of genomic medicine and thus a new approach must be developed. The specific objective of this research program is to develop and evaluate the Genomic Interpretation and Knowledge (GenIK) Framework for capturing interpretations related to clinical genomic results as standardized, structured data. The GenIK framework will: 1) facilitate more robust integration of genomic data into EHRs; 2) enable scalable knowledge management; 3) enable utilization of GIK by other systems; 4) enable clinical, translational, and discovery research; and 5) enable meaningful data sharing of GIK. Moreover, because the framework will be based on a generalized, standards-based knowledge model, it will elegantly extend to support new types of genomic interpretations and uses for those data. The GenIK framework will fill a critical gap in the representation, management, and delivery of GIK, thereby advancing genomic medicine. The proposed research program will develop foundational resources that will enable more seamless integration of genomics within both research and clinical settings. Research Area 1 will focus on discovery activities that support the development of the GenIK framework. Research Area 2 will focus on the translation and application of the GenIK framework within clinical systems. Research Area 3 will focus on dissemination to and engagement with stakeholder communities. The main hypothesis of this research is that implementations based on the GenIK framework will more fully capture GIK than existing approaches, thereby improving the management, use, and sharing of GIK. The Genomic Innovator Award will enable, in team-science projects and in collaboration with genomic research consortia, the study of the requirements for and implementation of scalable GIK management within clinical systems. The resources developed by the proposed research program will allow the capture and exchange of GIK to take place at the speed of discovery, supporting research in precision medicine as part of a learning healthcare system and accelerating scientific and medical breakthroughs that improve human health.

项目摘要/摘要 基因组医学的快速发展反映了我们对基因组学如何影响的理解的增加 健康。患者基因组信息为诊断和治疗提供信息的临床应用进展 决策正在推动检测和随后向电子设备报告基因组数据的增加 健康记录(EHR)和其他临床系统。生殖系基因组数据被推定为在 患者的生活，但对这些数据的解释及其临床应用将随着新的发现而继续发展 被制作并转化为临床实践。此外，对基因组数据的现代解释通常包括 其中一种解释被用来通知另一种解释的多步骤过程。时间上的动态性质 基因组信息和知识(GIK)以及解释之间的相互依赖构成了形式化的 知识模型对于那些数据来说是必不可少的。现有的表示和管理GIK的方法是不够的 为了完全支持基因组医学的需求，因此必须开发一种新的方法。 这项研究计划的具体目标是开发和评估基因组解释和 获取与临床基因组结果相关的解释的知识(Genik)框架 标准化的结构化数据。Genik框架将：1)促进基因组数据的更强大的整合 到EHR；2)实现可扩展的知识管理；3)使其他系统能够使用GIK；4) 支持临床、转换和发现研究；以及5)支持有意义的GIK数据共享。此外， 由于该框架将基于通用的、基于标准的知识模型，因此它将优雅地 扩展以支持对这些数据的新型基因组解释和使用。Genik框架将填补 GIK在表示、管理和交付方面的关键差距，从而推动基因组医学的发展。 拟议的研究计划将开发基础资源，使更多的无缝集成成为可能 基因组学在研究和临床环境中的应用。研究领域1将重点放在发现活动上 支持Genik框架的开发。研究领域2将重点放在翻译和 Genik框架在临床系统中的应用。研究领域3将侧重于传播到 以及与利益相关者社区的接触。这项研究的主要假设是实现 基于Genik框架将比现有方法更全面地捕获GIK，从而改进 管理、使用和共享GIK。 基因组创新者奖将在团队科学项目中并与基因组研究合作 联合会，研究临床中可扩展的GIK管理的要求和实施 系统。拟议的研究方案开发的资源将允许捕获和交换 GIK将以发现的速度进行，支持作为学习的一部分的精确医学研究 医疗保健体系和加快科学和医学突破，以改善人类健康。